Postdoctoral Appointee - AI and Computational Biology for Low-Dose Radiation Research

The Data Science Learning division is seeking a postdoctoral researcher to develop and apply artificial intelligence and computational biology methods to investigate the molecular and cellular effects of low-dose ionizing radiation. The position will contribute to the LUCID research program, a multi-institutional effort integrating experimental biology, machine learning, and mechanistic modeling to understand how human cells respond to low-dose and low-dose-rate radiation exposure.

The researcher will focus on analyzing and integrating large-scale cellular imaging, transcriptomic, and genomic datasets to identify radiation-induced cellular states, characterize dose- and time-dependent responses, and develop predictive models of cellular behavior. Particular emphasis will be placed on interpretable machine learning, multimodal representation learning, and the integration of experimental observations with biological mechanisms.

The position builds on established LUCID capabilities in longitudinal cellular imaging, RNA sequencing, and multimodal AI and will support the program's next phase of research in single-cell heterogeneity, radiation-induced adaptation, and long-term cellular fate.

Research responsibilities include:

- Multimodal biological data integration: Develop computational methods to integrate high-content fluorescence microscopy, Cell Painting-derived morphological features, transcriptomics, and other molecular measurements to characterize cellular responses to radiation.
- Machine learning and cellular-state modeling : Develop and evaluate machine-learning models, including representation-learning and foundation-model approaches, to identify cellular states and predict responses across radiation dose, dose rate, exposure duration, and cell type.
- Longitudinal cellular-response analysis: Analyze time-resolved experimental datasets to investigate cellular adaptation, DNA damage responses, senescence, recovery, and persistent changes following low-dose radiation exposure.
- Interpretable AI and biological discovery: Apply pathway-informed machine learning, statistical analysis, and explainable AI methods to identify molecular signatures, biological pathways, and candidate mechanisms associated with radiation-induced phenotypes.
- Model evaluation and generalization: Assess model robustness, uncertainty, and predictive performance across independent experiments, cell types, and exposure conditions. Compare AI-based predictions with established statistical and mechanistic baselines.
- Integration with experimental research: Collaborate with experimental biologists and computational scientists to formulate testable hypotheses, identify informative experimental measurements, and evaluate predictions against new experimental results.
- Scientific communication and research software: Publish findings in peer-reviewed journals, present research at scientific meetings, contribute to collaborative proposals and technical reports, and develop reproducible computational workflows and research software.

Position Requirements

- Completed PhD within the last 0-5 years, or soon to be completed and 0+ years of professional experience
- Ability to model Argonne's core values of impact, safety, respect, integrity and teamwork
- Experience developing and applying artificial intelligence and computational biology methods
- Ability to analyze and integrate large-scale cellular imaging, transcriptomic, and genomic datasets to identify radiation-induced cellular states
- Ability to develop predictive models of cellular behavior
- Experience with machine learning, multimodal representation learning, and the integration of experimental observations with biological mechanisms.

Job Family
Postdoctoral

Job Profile
Postdoctoral Appointee

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

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The expected hiring range for this position is $72,879.00-$121,465.00.
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Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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